Large-scale proteomics analysis identifies plasma protein biomarkers and potential therapeutic targets for rheumatoid arthritis: A prospective study in UK Biobank.

Abstract

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by symmetric joint swelling, pain, and progressive bone destruction. Although the advent of biologic therapies has significantly improved treatment outcomes, challenges remain in early detection and timely intervention. This study utilizes a nested case-cohort design from the UK Biobank (UKB), integrating proteomics, genome-wide association studies (GWAS), and single-cell RNA sequencing data from the GEO database to systematically evaluate proteins associated with RA risk and identify novel therapeutic targets. Through Cox analysis of proteomic data from 706 RA patients and 1410 controls, we identified 440 plasma proteins. Mendelian randomization analysis further narrowed down 35 plasma proteins, and colocalization analysis ultimately confirmed strong associations and colocalization for ICAM3, CTSV, and RNASET2 in the UKB-PPP dataset. Additionally, we developed an RA risk prediction model based on plasma proteins using the XGBoost algorithm, which demonstrated moderate performance (AUC = 0.74) with a prediction window of up to 5 years in advance. Furthermore, through functional enrichment analysis, protein-protein interaction (PPI) networks, and single-cell RNA sequencing, we elucidated the biological roles and mechanisms of these proteins in RA pathogenesis, providing new strategies for identifying biomarkers and developing targeted therapies for rheumatoid arthritis.